ArticleFrontiers in artificial intelligence2020
Identifying Ingredient Substitutions Using a Knowledge Graph of Food.
Article in Frontiers in artificial intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 43 citations in OpenAlex.
- Texture Independently Drives Liking in AI-Generated Alternative Protein Burgers.Foods (Basel, Switzerland) · 2026Article
- Artificial Intelligence-Enabled Ingredient Substitution in Food Systems: A Review and Conceptual Framework for Sensory, Functional, Nutritional, and Cultural Optimization.Foods (Basel, Switzerland) · 2025Review
- A review on knowledge graphs for healthcare: Resources, applications, and promises.Journal of biomedical informatics · 2025Review
- Big data analytics in food industry: a state-of-the-art literature review.NPJ science of food · 2025Review
- Adaptafood: an intelligent system to adapt recipes to specialised diets and healthy lifestyles.Multimedia systems · 2025Article
- Adaptive Constraint Relaxation in Personalized Nutrition Recommendations: An LLM-Driven Knowledge Graph Retrieval Approach.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Ki-Cook: clustering multimodal cooking representations through knowledge-infused learning.Frontiers in big data · 2023Article
- Applications of knowledge graphs for food science and industry.Patterns (New York, N.Y.) · 2022Review
- Food Recipe Ingredient Substitution Ontology Design Pattern.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
People can affect change in their eating patterns by substituting ingredients in recipes. Such substitutions may be motivated by specific goals, like modifying the intake of a specific nutrient or avoiding a particular category of ingredients. Determining how to modify a recipe can be difficult because people need to 1) identify which ingredients can act as valid replacements for the original and 2) figure out whether the substitution is "good" for their particular context, which may consider factors such as allergies, nutritional contents of individual ingredients, and other dietary restrictions. We propose an approach to leverage both explicit semantic information about ingredients, encapsulated in a knowledge graph of food, and implicit semantics, captured through word embeddings, to develop a substitutability heuristic to rank plausible substitute options automatically. Our proposed system also helps determine which ingredient substitution options are "healthy" using nutritional information and food classification constraints. We evaluate our substitutability heuristic, diet-improvement ingredient substitutability heuristic (DIISH), using a dataset of ground-truth substitutions scraped from ingredient substitution guides and user reviews of recipes, demonstrating that our approach can help reduce the human effort required to make recipes more suitable for specific dietary needs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.